Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference

• Published in: Biological psychiatry : cognitive neuroscience and neuroimaging Vol. 8; no. 11; pp. 1075 - 1083
• Main Authors: Liloia, Donato, Cauda, Franco, Uddin, Lucina Q., Manuello, Jordi, Mancuso, Lorenzo, Keller, Roberto, Nani, Andrea, Costa, Tommaso
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2023
• Subjects: Activation likelihood estimation; Bayes factor modeling; Cerebellum; Forward inference; Medial frontoparietal network; Structural MRI; Cerebellum; Structural MRI; Bayes factor modeling; Medial frontoparietal network; Activation likelihood estimation; Forward inference
• ISSN: 2451-9022; 2451-9030; 2451-9030
• DOI: 10.1016/j.bpsc.2022.01.007

Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.